A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics

• Published in: Applied sciences Vol. 14; no. 24; p. 11489
• Main Authors: Moghbelan, Yasamin, Esposito, Alfonso, Zyrianoff, Ivan, Spaletta, Giulia, Borgo, Stefano, Masolo, Claudio, Ballarin, Fabiana, Seidita, Valeria, Toni, Roberto, Barbaro, Fulvio, Di Conza, Giusy, Quartulli, Francesca Pia, Di Felice, Marco
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: Automation; Demographics; Error correction & detection; Exercise; Feedback; Heart rate; human activity recognition; humanoid robotics; Internet of Things; Knowledge representation; Machine learning; Medical research; Motion capture; motor rehabilitation; Ontology; Oxygen saturation; Patients; Physical therapy; Physiology; Posture; Rehabilitation; Robotics; Robotics industry; Robots; Sensors; Software; Stroke; Therapeutics, Physiological; Vital signs; Wearable computers; Italy
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app142411489

The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects.